CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id uint32 0 1.66k | title stringlengths 13 147 | authors listlengths 1 17 | cvf_url stringlengths 90 195 | pdf_url stringlengths 91 196 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 305 619 | abstract large_stringlengths 449 1.99k | embedding listlengths 768 768 |
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0 | Invertible Denoising Network: A Light Solution for Real Noise Removal | [
"Yang Liu",
"Zhenyue Qin",
"Saeed Anwar",
"Pan Ji",
"Dongwoo Kim",
"Sabrina Caldwell",
"Tom Gedeon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.pdf | null | 2104.10546 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Yang and Qin, Zhenyue and Anwar, Saeed and Ji, Pan and Kim, Dongwoo and Caldwell, Sabrina and Gedeon, Tom},
title = {Invertible Denoising Network: A Light Solution for Real Noise Removal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying invertible models to remove noise is challenging because the input is noisy, and the reversed output is clean, following two different distribution... | [
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1 | Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction | [
"Bohan Wu",
"Suraj Nair",
"Roberto Martin-Martin",
"Li Fei-Fei",
"Chelsea Finn"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Greedy_Hierarchical_Variational_CVPR_2021_supplemental.pdf | 2103.04174 | title_snapshot | @InProceedings{Wu_2021_CVPR,
author = {Wu, Bohan and Nair, Suraj and Martin-Martin, Roberto and Fei-Fei, Li and Finn, Chelsea},
title = {Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on la... | [
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2 | Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks | [
"Roi Pony",
"Itay Naeh",
"Shie Mannor"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pony_Over-the-Air_Adversarial_Flickering_CVPR_2021_supplemental.pdf | 2002.05123 | cvf | @InProceedings{Pony_2021_CVPR,
author = {Pony, Roi and Naeh, Itay and Mannor, Shie},
title = {Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation... | [
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3 | Encoder Fusion Network With Co-Attention Embedding for Referring Image Segmentation | [
"Guang Feng",
"Zhiwei Hu",
"Lihe Zhang",
"Huchuan Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | null | 2105.01839 | cvf | @InProceedings{Feng_2021_CVPR,
author = {Feng, Guang and Hu, Zhiwei and Zhang, Lihe and Lu, Huchuan},
title = {Encoder Fusion Network With Co-Attention Embedding for Referring Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recently, referring image segmentation has aroused widespread interest. Previous methods perform the multi-modal fusion between language and vision at the decoding side of the network. And, linguistic feature interacts with visual feature of each scale separately, which ignores the continuous guidance of language to mu... | [
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4 | Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo | [
"Seung-Hwan Baek",
"Felix Heide"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Baek_Polka_Lines_Learning_CVPR_2021_supplemental.zip | 2011.13117 | cvf | @InProceedings{Baek_2021_CVPR,
author = {Baek, Seung-Hwan and Heide, Felix},
title = {Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Active stereo cameras that recover depth from structured light captures have become a cornerstone sensor modality for 3D scene reconstruction and understanding tasks across application domains. Active stereo cameras project a pseudo-random dot pattern on object surfaces to extract disparity independently of object text... | [
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5 | Image Inpainting With External-Internal Learning and Monochromic Bottleneck | [
"Tengfei Wang",
"Hao Ouyang",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Image_Inpainting_With_CVPR_2021_supplemental.zip | 2104.09068 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Tengfei and Ouyang, Hao and Chen, Qifeng},
title = {Image Inpainting With External-Internal Learning and Monochromic Bottleneck},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | Although recent inpainting approaches have demonstrated significant improvement with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottle... | [
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6 | Patch2Pix: Epipolar-Guided Pixel-Level Correspondences | [
"Qunjie Zhou",
"Torsten Sattler",
"Laura Leal-Taixe"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_CVPR_2021_supplemental.pdf | 2012.01909 | title_snapshot | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Qunjie and Sattler, Torsten and Leal-Taixe, Laura},
title = {Patch2Pix: Epipolar-Guided Pixel-Level Correspondences},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. Recently emerged correspondence networks propose to perform those steps inside a single network but suffer from low matching resol... | [
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7 | Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer | [
"Yulin Li",
"Jianfeng He",
"Tianzhu Zhang",
"Xiang Liu",
"Yongdong Zhang",
"Feng Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Diverse_Part_Discovery_CVPR_2021_supplemental.pdf | 2106.04095 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Yulin and He, Jianfeng and Zhang, Tianzhu and Liu, Xiang and Zhang, Yongdong and Wu, Feng},
title = {Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a t... | [
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8 | Counterfactual Zero-Shot and Open-Set Visual Recognition | [
"Zhongqi Yue",
"Tan Wang",
"Qianru Sun",
"Xian-Sheng Hua",
"Hanwang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yue_Counterfactual_Zero-Shot_and_CVPR_2021_supplemental.pdf | 2103.00887 | cvf | @InProceedings{Yue_2021_CVPR,
author = {Yue, Zhongqi and Wang, Tan and Sun, Qianru and Hua, Xian-Sheng and Zhang, Hanwang},
title = {Counterfactual Zero-Shot and Open-Set Visual Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea stems from the observation that the generated samples for unseen-classes are often out of the true dis... | [
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9 | Person30K: A Dual-Meta Generalization Network for Person Re-Identification | [
"Yan Bai",
"Jile Jiao",
"Wang Ce",
"Jun Liu",
"Yihang Lou",
"Xuetao Feng",
"Ling-Yu Duan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Bai_2021_CVPR,
author = {Bai, Yan and Jiao, Jile and Ce, Wang and Liu, Jun and Lou, Yihang and Feng, Xuetao and Duan, Ling-Yu},
title = {Person30K: A Dual-Meta Generalization Network for Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limitations in multiple ... | [
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